"Health Outcomes Research: bridging emerging medical technologies and real-world cancer care" and "Gynecological Cancer: Translational Science and Pivotal Trials"

Transcript

• 00:00Today we have two speakers and our
• 00:02first speaker is Michaela Dine-in,
• 00:05who's an associate professor
• 00:06Epidemiology and Co leader of the
• 00:09Yale Cancer Center Cancer Prevention
• 00:11and Control Research program.
• 00:13She joined us from Duke University last
• 00:15year and is a Health Sciences features
• 00:18researcher specializing in using
• 00:20epidemiological methodologies to study
• 00:22complex datasets with particular expertise
• 00:24and leveraging existing real-world
• 00:26datasets to examine cancer outcomes.
• 00:30Is also a leading researcher lean,
• 00:33and then I NCI funded study
• 00:36looking at health disparities
• 00:37in patients with kidney cancer.
• 00:39And so I think we'll hear
• 00:40about some of that today.
• 00:41So Michaela welcome and I
• 00:43have to have to unmute.
• 00:48Great, just pulling up my slides here.
• 00:53OK, looks like we're ready to rock and roll.
• 00:56Alright so thank you so much.
• 00:58Good afternoon everyone.
• 00:59I'm actually in Chicago right now and
• 01:02attending the Astro annual meeting.
• 01:04So technically it's still morning here,
• 01:07but either way I'm delighted to
• 01:09be speaking with you today so.
• 01:11Uhm, as was mentioned,
• 01:13I'm a health outcomes researcher by training
• 01:15and I can bucket my current research
• 01:17projects into three broad categories,
• 01:19including emerging technology in oncology,
• 01:21survivorship,
• 01:22and patient outcomes and molecular
• 01:25oncology outcomes research.
• 01:27But the running theme throughout
• 01:28these example projects is leveraging
• 01:30real-world data to answer questions
• 01:32about dissemination outcomes,
• 01:33costs and disparities,
• 01:34and how I think about answering
• 01:37these types of questions using
• 01:39real-world data resources.
• 01:40So what is the value added?
• 01:42Of health outcomes research and while
• 01:45RCT's are considered higher up in
• 01:47the food chain than cohort and case
• 01:49control studies in the traditional
• 01:51levels of evidence pyramid shown here,
• 01:53there are many types of questions
• 01:55that are not feasible to examine
• 01:57in the context of a trial,
• 01:58but that are feasible within health outcomes,
• 02:01study methodologies,
• 02:02and here are some examples of the
• 02:04types of questions we can answer about
• 02:07emerging diagnostics and therapeutics
• 02:09using real-world data resources.
• 02:11Randomized trials are required.
• 02:13Approval of a novel therapeutic agent,
• 02:15but approvals of diagnostics and
• 02:17other biomarkers are more complex
• 02:19and not always evaluated by ARC.
• 02:21Prior to their approval or
• 02:23coverage by insurance.
• 02:25However, even for therapeutic agents,
• 02:27initial approvals often arise from
• 02:30RCT comparisons with another single
• 02:32treatment which may be outdated
• 02:34by the time approvals received.
• 02:36In reality,
• 02:37more and more cancers.
• 02:39Have increasing numbers of possible
• 02:41treatment options and combinations
• 02:42and it's just not feasible to
• 02:44examine all possible treatment
• 02:45strategies in a head-to-head fashion,
• 02:47and oftentimes there's honestly
• 02:49not adequate financial incentives
• 02:51to support such trials.
• 02:52We also know that patients who participate
• 02:55in RCT's differ systematically from
• 02:57the average real world patient,
• 02:59where life and treatment is just
• 03:01a lot messier as compared to the
• 03:03highly curated patient population
• 03:05and controlled environment of an RCT.
• 03:06And this is an example study,
• 03:08not mine of a patient of patients
• 03:10with primary CNS lymphoma treated at
• 03:12the same institution who received the
• 03:15same treatment both on and off protocol,
• 03:17and the investigators showed that
• 03:19patients who were treated in the real
• 03:21world practice meaning off protocol.
• 03:23Or older,
• 03:23sicker had worse disease and had
• 03:25dramatically worse survival than
• 03:27the patients who were treated
• 03:28on the clinical trial.
• 03:30So here I have presented an
• 03:31overview of many different types
• 03:33of data that can be used to conduct
• 03:35real-world health outcomes research,
• 03:37and what I really want to drive home
• 03:38is that it's important to remind folks
• 03:40that there is no perfect single data set.
• 03:42But by leveraging the major strengths
• 03:44and weaknesses of different data,
• 03:46different types of datasets
• 03:47as they currently exist,
• 03:49or improving upon them,
• 03:50we can answer some pretty cool questions.
• 03:53So this is an example of
• 03:54a past fully completed
• 03:55study that I conducted in breast cancer,
• 03:57and this was a five year study
• 03:59that was funded by AHRQ.
• 04:00Where we were looking at adoption,
• 04:01chemotherapy, use and costs
• 04:03associated with Oncotype DX,
• 04:04in brand and breast cancer and a
• 04:07lot has changed in the subsequent
• 04:09years since this work was completed,
• 04:10but at the time in CC and guidelines.
• 04:12Recommended consideration of
• 04:13chemotherapy and all of early stage
• 04:16disease patients with primary tumors
• 04:18greater than one centimeter node.
• 04:19Negative ER positive disease,
• 04:21and patients characteristics that
• 04:22were consistent with chemotherapy.
• 04:24Candidacy and uncle Type DX was still
• 04:26relatively new to the scene at this time,
• 04:28and no one had looked at its
• 04:29use in real world population.
• 04:30Case studies.
• 04:31So let's consider the gaps in
• 04:33knowledge that existed at the time,
• 04:35so we know that randomized trials
• 04:37had confirmed the prognostic and
• 04:39predictive value of Oncotype DX,
• 04:40and there had been some single
• 04:42institution series that suggested
• 04:44that decreased chemotherapy was
• 04:45associated with archetype DX use.
• 04:48However,
• 04:48there hadn't been any nationally
• 04:50representative studies conducted.
• 04:51There were still questions about whether
• 04:53or not the adoption and diffusion of
• 04:56Archetype DX was being done equitably
• 04:57across different subgroups in the population,
• 05:00and there are questions about
• 05:01the impact that.
• 05:02Architect DX was having on chemotherapy,
• 05:04utilizations and costs.
• 05:04In the real world.
• 05:06And finally,
• 05:06there was limited data on patients
• 05:08who are 65 years and older.
• 05:09Because these were underrepresented
• 05:11in any of the child data.
• 05:14So in thinking about the types of
• 05:16questions about architects that
• 05:17I was interested in looking at,
• 05:19I chose to use the seer Medicare linked data,
• 05:21which combines the detailed clinical
• 05:23pathologic data from this year
• 05:25registry with the LOGITUDINAL
• 05:27claims from the Medicare data.
• 05:29So we use the Medicare claims portion
• 05:31of the SEER Medicare data to detect the
• 05:33use of Oncotype DX in our study population.
• 05:36Now,
• 05:36there was no specific CPT procedure
• 05:39code for Oncotype DX.
• 05:40In fact,
• 05:41the test is build using the CPT code 84999.
• 05:45Defined as unlisted chemistry procedure.
• 05:48However,
• 05:48using the knowledge that all Oncotype
• 05:50DX tests are processed by single
• 05:53provider in a single location,
• 05:55we were able to use an algorithm to
• 05:57detect the archetypes DX code in the
• 05:59Medicare claims data and confirm
• 06:01that all tests were performed by the
• 06:03same single provider from the same
• 06:05single location with 95% of these
• 06:08tests having identical payment of $3414.
• 06:11So this was considered a very
• 06:13creative approach at the time.
• 06:14Again, this was a while ago,
• 06:15and.
• 06:16And I believe ultimately,
• 06:17this creative approach is what
• 06:19got the study funded,
• 06:20but I've seen this approach recreated
• 06:22for other diagnostics many times signs.
• 06:23And this is just a side note to suggest
• 06:25that if you can think of novel ways to use
• 06:27data that have been around a long time,
• 06:29you can still make real
• 06:30contributions to the field.
• 06:31Interestingly,
• 06:32the Seer Medicare data now actually
• 06:34includes the Oncotype DX rescored
• 06:36data in the data set itself,
• 06:38but back then this data was
• 06:40not publicly available,
• 06:41so we were only able to detect
• 06:43receipt of testing at the time,
• 06:44but did not know what the test results.
• 06:46Actually were so we were able to show
• 06:48that archetype decks used in the real
• 06:50world increased over the study period,
• 06:52particularly with in the younger age
• 06:54group in the SEER Medicare data.
• 06:57And since the use of Oncotype DX was
• 06:58supposed to inform whether or not
• 07:00a patient received chemotherapy,
• 07:01we wanted to see how often the the
• 07:03use of diagnostic or sorry we wanted
• 07:05to see how the use of the diagnostic
• 07:08was impacting the use of chemotherapy.
• 07:10And here we can see that in patients
• 07:12who would traditionally be considered
• 07:13high risk due to their tumor size or stage,
• 07:15that chemotherapy.
• 07:16He's appeared to decline following
• 07:19the introduction of architect Deacs.
• 07:21So in multivariable analysis,
• 07:22we did not see an overall association
• 07:24between receipt of Archetype DX
• 07:26and receipt of chemo.
• 07:28However,
• 07:28we did see that patients with
• 07:30clinical markers of more aggressive
• 07:32disease such as tumor size,
• 07:34grade and NCCN,
• 07:35defined clinical pathologic risk had an
• 07:38increased likelihood of receiving chemo.
• 07:40The most nuanced and interesting finding,
• 07:43however,
• 07:43was that when we looked at the
• 07:46interaction between receipt of Oncotype
• 07:48DX and NCCN defined clinical risk,
• 07:50we saw that.
• 07:52Receipt of Oncotype DX was associated
• 07:54with decreased chemo in NCCN
• 07:56high risk patients and increased
• 07:59chemo and NCCN low risk patients.
• 08:01So at the time it was a foregone
• 08:03conclusion by many that the use of
• 08:05Oncotype DX would not only be cost effective,
• 08:08but also costs saving.
• 08:10However,
• 08:10there was a meta analysis of the
• 08:13ability of AC type DX to reduce costs,
• 08:15and it revealed that there was
• 08:17a wide range in the perceived
• 08:19benefit cost benefits of archetype
• 08:21deacs according to weather.
• 08:22A study had been funded by Genomic Health.
• 08:24The sponsor, which is those studies,
• 08:27are shown in blue on this graph.
• 08:29As opposed to other funding sources.
• 08:31So interestingly,
• 08:32the five studies that suggested
• 08:35Archetype DX was cost saving were
• 08:37all funded by genomic health.
• 08:39Ultimately,
• 08:39however,
• 08:40these were all modeling studies and we
• 08:42wanted to try to look at real-world data,
• 08:44so this is important,
• 08:45because when you look closely
• 08:46at these modeling studies,
• 08:4818 of them assume that T stage
• 08:51and tumor grade had no impact
• 08:53on chemotherapy decisions,
• 08:54which we clearly saw in the data
• 08:56I showed previously was not the
• 08:57case in our real-world data,
• 08:59and only five studies.
• 09:00Accounting for the fact that
• 09:01architect at DX testing might
• 09:03actually increase chemotherapy use
• 09:05in clinically low risk patients.
• 09:06So what did we find when we looked
• 09:08at costs associated with Oncotype
• 09:10DX in the real world setting?
• 09:12So the main takeaway lesson was that
• 09:14the impact of these tests depends
• 09:16strongly on the patient population
• 09:18and pretest likelihood that a patient
• 09:20was going to get chemotherapy anyway.
• 09:23So in patients who were
• 09:24planned for chemo or high
• 09:25risk patients, Oncotype DX can
• 09:28can reduce costs, chemo and costs.
• 09:31However, for lower intermediate patients,
• 09:33there is no evidence that Oncotype
• 09:35DX will reduce costs in actuality.
• 09:38And it's it's use is actually
• 09:40associated with higher non cancer costs,
• 09:42likely due to just general
• 09:45overall increased health care
• 09:46utilization in this population.
• 09:48And then finally using these same data,
• 09:50we were able to look at questions
• 09:52regarding what physician or provider
• 09:54characteristics were associated
• 09:55with the use of archetype DX and
• 09:58what we saw was that about 70% of
• 10:00patients who were receiving Oncotype
• 10:01DX had the Oncotype DX test ordered
• 10:04by their medical oncologists.
• 10:05But we were also able to look at
• 10:08factors physician characteristics
• 10:09that were associated with increased
• 10:11likelihood of receiving Oncotype
• 10:13DX and these were having been seen
• 10:14by a surgical oncologist having
• 10:16been seen having had your surgery
• 10:18at an academic Medical Center.
• 10:20Having been treated by a female medical
• 10:23oncologist and having been treated by
• 10:25a medical oncologist who was within
• 10:27five years of finishing their training.
• 10:29So I'm going to move on to my next example,
• 10:31which is from my current NCI funded
• 10:33R 01 where we are examining access
• 10:35and adherence to oral anti cancer
• 10:37agents and drivers of real world
• 10:40disparities in patients with metastatic
• 10:41renal cell carcinoma.
• 10:43As is the case in many cancers,
• 10:46the number of available therapies for kidney
• 10:49cancers have expanded dramatically over
• 10:51the past decade and a half and interestingly,
• 10:54ten of these therapy,
• 10:55ten of the therapies approved
• 10:56between 2005 and 2016.
• 10:58Of those 10.
• 11:00Seven of them were oral agents and we
• 11:02can use real world data to look at
• 11:04issues pertaining to patients ability
• 11:06to access and then stay adherent to
• 11:09these potentially lifesaving drugs.
• 11:10So once again,
• 11:12let's take a look at what what was
• 11:14known versus the knowledge gaps
• 11:15surrounding a a use in patients
• 11:17with kidney cancer at the time.
• 11:19So we know we knew that oral anti
• 11:21cancer agents and we know that they
• 11:23pose unique challenges to delivery and
• 11:25also there was clinical trial data
• 11:27that showed increased progression,
• 11:29free survival and overall survival
• 11:31for several different ways and
• 11:33typically always have shown to have a
• 11:36more favorable toxicity profile than
• 11:39traditional cytotoxic chemotherapies.
• 11:41However their continued.
• 11:42To be gaps in the knowledge
• 11:44around whether outcomes,
• 11:45what outcomes and toxicities looked like
• 11:48in older and comorbid patient populations,
• 11:50there were few head-to-head OA
• 11:53comparisons and there were additional
• 11:55unknown adherence barriers as well
• 11:57as impacts of out of cost out of
• 12:00pocket costs on adherence and how
• 12:02the impact of non what the impact
• 12:05of nonadherence had on outcomes
• 12:07for these patients.
• 12:08So for this study,
• 12:09we once again decided to leverage
• 12:10the strengths of the Seer,
• 12:12Medicare and the Medicare claims data,
• 12:14and in this case, Medicare Part D,
• 12:16which is includes prescription drug claims,
• 12:18was crucial for this study.
• 12:20But we also added an additional data
• 12:22source called the North Carolina Cypher
• 12:24data now North Carolina Cypher is an
• 12:26example of a state cancer registry
• 12:28that's been linked to claims data,
• 12:29and in this case it's the North
• 12:31Carolina Cancer Registry data that has
• 12:34been linked to Medicare, Medicaid,
• 12:35and Blue Cross Blue Shield data.
• 12:37So you can see here.
• 12:39That strengths include the same
• 12:40detailed clinical pathologic data that's
• 12:42contained in the SEER Medicare data set.
• 12:44But for patients of all ages,
• 12:46we receive Medicare is limited to
• 12:48those who are 65 years and older and
• 12:50with unsafe Cypher has patients with
• 12:52different types of insurance coverage.
• 12:54Where senior Medicare is limited,
• 12:55obviously, to just the Medicare population.
• 12:59So here I show the seer Medicare rates
• 13:01of utilization of oral anti cancer
• 13:03agents in patients with renal cell
• 13:05carcinoma and we also reproduce this
• 13:07data in the North Carolina cypher
• 13:09data where we saw highly similar
• 13:12trajectories and rates of OH agents.
• 13:14We found that roughly 1/3 of patients
• 13:16were receiving an oral anti cancer
• 13:17agent at all within a year of being
• 13:19diagnosed with advanced disease and
• 13:21that the majority of these patients
• 13:23were initially treated with sunitinib.
• 13:25A multivariable analysis of CR Medicare
• 13:28factors associated with utilization
• 13:29did not show evidence of differential
• 13:32receipt of oral therapies by patient race,
• 13:34ethnicity, or socioeconomic status.
• 13:36However,
• 13:37we did see decreased utilization
• 13:39in patients who were unmarried,
• 13:40older, or that lived in the South.
• 13:43So one of the strengths of the
• 13:45North Carolina cipher data is that
• 13:46it includes adults of all ages
• 13:47as well as private insurance.
• 13:49As I've already mentioned before,
• 13:51we adjusted for age.
• 13:52There were large differences in utilization
• 13:54by private versus Medicare insurance.
• 13:57However, in multivariable adjusted analysis,
• 13:59we saw that there was no difference
• 14:01in the utilization by insurance.
• 14:02Instead,
• 14:03this was likely driven entirely by age,
• 14:05with older patients being less
• 14:06likely to receive therapy.
• 14:08We also observed that frailty and
• 14:10having multiple kohram abilities
• 14:12were both associated with.
• 14:14Decrease to a utilization.
• 14:15And lastly we looked at patients with
• 14:17all stages of kidney cancer and saw
• 14:20that patients who were diagnosed with
• 14:22stage one disease but that experienced
• 14:24progression to metastatic disease were
• 14:26less likely to utilize Inoue within
• 14:28a year of metastatic disease diagnosis,
• 14:31and this is likely due to slower
• 14:33growing disease with a less urgent
• 14:36need to treat immediately.
• 14:37Come for oral anti cancer agents.
• 14:39However,
• 14:39it's important to remember that
• 14:41in addition to utilization,
• 14:42there's also the concept of adherence
• 14:45or the percentage of time a patient
• 14:47was taking their anti cancer drug.
• 14:49We know that in general,
• 14:50adherence to oral medications is often
• 14:53far from 100% due to any number of
• 14:55reasons such as side effects or costs.
• 14:58We looked at adherence in both the
• 15:00Seer Medicare and the Cypher cohorts
• 15:02and we observed slightly higher
• 15:04rates of adherence within the North
• 15:07Carolina cypher patient population.
• 15:09As compared to the CR Medicare cohort,
• 15:11we think this is largely due to the
• 15:13difference in age between the cohorts.
• 15:15As both cohorts showed evidence
• 15:16of either older patients or
• 15:18those with Medicare insurance
• 15:20having lower adherence rates.
• 15:21North Carolina Cypher was somewhat limited
• 15:23in power due to the smaller sample sizes,
• 15:26and it did not examine adherence
• 15:28by by different agents in
• 15:30the multivariable analysis.
• 15:31However, there was evidence of substantially
• 15:34lower adherence to soften it in both cohorts.
• 15:36We saw a strong impact of poverty on
• 15:39adherence within the SEER Medicare data,
• 15:41but not the North Carolina cypher data.
• 15:43And although it is unclear why,
• 15:44we hypothesize that older patients
• 15:46living on a fixed income may be more
• 15:50sensitive to financial stressors.
• 15:51Consistent with this,
• 15:52we saw that OAS,
• 15:54with out of pocket costs over $200,
• 15:57were associated with decreased adherence
• 15:59within the SEER Medicare cohort.
• 16:02So these real world datasets also
• 16:03allow you to look at survival.
• 16:05And here is a three month landmark survival
• 16:08curve of all 'cause mortality for a pass.
• 16:10Open abusers by whether
• 16:12they received the trial.
• 16:13Recommended dose of 800 milligrams of
• 16:16pheasant per day in the three months
• 16:19following a a initiation for the
• 16:21patients getting the prescribed dose
• 16:23for the first three months of treatment,
• 16:24we saw superior outcomes and survival
• 16:26was assessed beginning at three
• 16:28months post postoperative initiation.
• 16:30In order to avoid introducing.
• 16:32Immortal time bias in the analysis.
• 16:35So I think it's incredibly critical to
• 16:37acknowledge that a key limitation of
• 16:38all these data sets is that the patient
• 16:41perspective and the patient voice is missing.
• 16:43I also feel it's incredibly important to
• 16:45do our best to include this perspective,
• 16:47even when working exclusively
• 16:49with secondary data,
• 16:50and one way that we address this
• 16:52for the renal cell carcinoma.
• 16:53A study was by partnering with patient
• 16:56advocacy groups who helped us identify
• 16:58questions that were most important to them.
• 17:01So,
• 17:01for example,
• 17:02these patients and their families,
• 17:04they wanted to know how often providers
• 17:05were switching their medications.
• 17:07Which is something we hadn't
• 17:08planned on examining,
• 17:09but we were absolutely capable of
• 17:11examining in our real-world data set.
• 17:13So we looked at the request of the patients,
• 17:16and we found that while only 6%
• 17:18of RCC patients switched aways
• 17:20within 90 days of diagnosis,
• 17:23that number increased to 20% of RCC patients,
• 17:26switched to their always
• 17:27within one year of diagnosis.
• 17:30So now I'd like to move on to an example
• 17:32of current future work that I'm doing.
• 17:34So I was recently awarded in American
• 17:36Cancer Society 5 year Research Scholar
• 17:38Grant and this grant will be developing
• 17:40algorithms to inform risk stratified
• 17:42care for long term cancer survivors.
• 17:44So this figure was modified from a
• 17:46paper by Effinger and McCabe which
• 17:48shows at the top the current model,
• 17:51care for cancer survivors,
• 17:52which is more of a one size
• 17:54fits all approach.
• 17:55Once the patient is diagnosed
• 17:57with their cancer,
• 17:58their care is transferred to an oncologist
• 18:00for an indefinite period of time.
• 18:02Little to no ongoing participation
• 18:04from the PCP.
• 18:05The bottom shows the proposed
• 18:07shared practice model care based
• 18:09on risk stratification,
• 18:10which helps to inform
• 18:11the point in time when a
• 18:12cancer survivors care might be
• 18:14appropriately transferred back to you or
• 18:16shared with the primary care physician
• 18:18with the idea being that the new
• 18:20model represents both a more efficient
• 18:22and better quality model of care.
• 18:24So this figure is from a study
• 18:26where McConnell and colleagues used
• 18:28National Cancer Registry data from
• 18:29the UK and Northern Ireland tourist
• 18:32stratify patients with twenty of
• 18:33the most common cancers into three
• 18:36groups based on overall survival at
• 18:38one in five years from diagnosis.
• 18:40And this is just to demonstrate that
• 18:43crude risk categorization is possible
• 18:44and is currently being used to
• 18:46inform treatment in other countries.
• 18:48So the authors noted that important
• 18:49caveats of this analysis included
• 18:51the absence of treatment information
• 18:52which was not available, and.
• 18:54That their data was unable to assess
• 18:56treatment related complications,
• 18:58both of which I propose to improve
• 19:00upon in our models for this ACS grant.
• 19:02So once again,
• 19:03we return to existing currently
• 19:05existing knowledge gaps,
• 19:07which real-world data and outcome
• 19:08methodologies can help to address,
• 19:10so we know that Uncle logic and
• 19:12noncaloric risks vary substantially by
• 19:14cancer stage and treatment and cancer type.
• 19:17We also know that cancer site
• 19:19and stage alone can provide broad
• 19:21uncle logic risk categories.
• 19:22However, non uncle logic disease.
• 19:26Risks have been defined qualitatively,
• 19:28but not quantitatively,
• 19:30and cancer survivors.
• 19:32And we do not know how Uncle Logic
• 19:35and on non uncle logic risks compare
• 19:38or compete within cancer survivors.
• 19:41And there's also a need to estimate
• 19:44these risks at the point of care.
• 19:46So we will once again use this year
• 19:48Medicare and the North Carolina cipher data.
• 19:50But the new data set addition to
• 19:52this project will be incorporating
• 19:54data from the Veterans Health system
• 19:56and the overarching plan is to use
• 19:59inputs that are available from
• 20:00all three of these datasets,
• 20:02such as cancer or specific variables
• 20:04like site and stage treatment.
• 20:06Personal characteristics like age
• 20:08and gender and race and ethnicity,
• 20:10and then aging related concerns like
• 20:13comorbidities and functional status
• 20:15to develop risk prediction models.
• 20:17In breast, breast,
• 20:18prostate and colorectal cancers.
• 20:20To predict both ankle logic and
• 20:22non oncologic events,
• 20:23for which long term cancer
• 20:25survivors are at increased risk.
• 20:27So these risk algorithm algorithms will
• 20:29separate long term cancer survivors into low,
• 20:31medium and high risk categories to
• 20:34help inform discussions between
• 20:35survivors and physicians about their
• 20:37optimal care going forward and
• 20:39ultimately the final product will be
• 20:41a freely available web calculator in
• 20:43which patients and or physicians can
• 20:45input their individual information
• 20:47to help categorize their individual
• 20:49risk and inform pathways of care.
• 20:52So next on the horizon for me is
• 20:54tackling additional unmet needs of
• 20:56traditional health services research
• 20:58through novel data linkages and I'm
• 21:00developing studies that will include
• 21:02actual physical tumor samples so
• 21:04that we can run genomic sequence
• 21:06analysis on them and then link that
• 21:08additional biologic information
• 21:10to both tumor registry data and
• 21:13longitudinal claims data.
• 21:14So there are a couple existing
• 21:16resources which
• 21:16I have already tapped into to get this
• 21:18work off the ground and the first of which
• 21:21is the SEER residual tissue repository,
• 21:22which is a program that used to be funded
• 21:25by NCI to maintain physical tumor samples
• 21:27for patients contained in the SEER
• 21:30registry for three participating sites,
• 21:32which were Iowa, Hawaii and Los Angeles, CA.
• 21:34So like I said, the program
• 21:38consists of pathologic specimens.
• 21:40These are old specimens were
• 21:42collected between 1992 and 2006.
• 21:44I've already.
• 21:45Mention the participating see registries,
• 21:47but they do allow the ability to
• 21:50physically analyze tumor samples and So
• 21:52what I did was we recently completed a
• 21:55proof of concept study on a very small
• 21:58breast cancer cohort to demonstrate
• 22:00the process for combining the sear,
• 22:02the Medicare,
• 22:03and the genomic or biologic data obtained
• 22:05from running gene expression analysis
• 22:07on the tumor samples themselves.
• 22:09So unfortunately,
• 22:10LA did not participate in this pilot study
• 22:12due to an inability to procure large enough.
• 22:15Funds to cover their participation
• 22:16costs and this left us with two
• 22:19very distinct and racially and
• 22:21ethnically homogeneous populations
• 22:22which were not was not ideal.
• 22:24We would have liked it to have
• 22:25been much more representative,
• 22:26but it did allow us to proceed with the
• 22:29proof of concept study and here is a brief
• 22:32summary of some of our major findings,
• 22:34so this publication is in press and
• 22:36will be published in two days in JAMA
• 22:38Network and I'm happy to share that
• 22:39publication with folks to go through
• 22:41in more detail once it's published.
• 22:43But you can see that our major findings.
• 22:46Really show how we were able to
• 22:48leverage the different aspects of
• 22:50these three different data linkages.
• 22:52The three different datasets that
• 22:53we linked together so we were able
• 22:55to show from the Medicare claims
• 22:57data that symptomatic detection of
• 22:58breast cancer was associated with
• 23:00a higher mortality hazards ratio
• 23:02as from the SEER registry data.
• 23:04We were able to show that.
• 23:07Low levels of high school graduation
• 23:09rates were associated with a higher
• 23:11mortality mortality hazard ratio and
• 23:13then from the tumor samples and the
• 23:15genetic analysis that we conducted on these,
• 23:18we were able to show that androgen
• 23:20receptor macrophage set of toxicity and T.
• 23:22Rex signaling were all associated
• 23:24with reduced mortality.
• 23:25But the key thing that I want to
• 23:27highlight here is that factors
• 23:29related to socioeconomic status and
• 23:31screening access remained associated
• 23:33with mortality even after adjusting
• 23:34for clinical and genomic factors.
• 23:38So what does the future look like
• 23:40for this work?
• 23:40Well,
• 23:41I'm getting ready to submit a
• 23:42narrow one which would leverage the
• 23:45sear virtual tissue repository and
• 23:47proposes the first in kind linkage
• 23:48ever of the tumor samples with ceron,
• 23:51Medicare longitudinal claims.
• 23:53So the server consists of
• 23:55seven participating.
• 23:56See registry, so we're up to 7 from 3,
• 23:59and the pathologic specimen location
• 24:01is known for the most recent 10 years.
• 24:04So this is, this is the the oldest.
• 24:06The tissue samples are ten years old.
• 24:09But the collection is ongoing,
• 24:10so these are recent tissues.
• 24:12And once again we must physically
• 24:13request and fund the acquisition
• 24:15of the pathologic specimens from
• 24:17the pathology labs storing them.
• 24:18But what are we proposing to do? So?
• 24:20We're calling this a retro genomic approach,
• 24:23which we are defining as a combination
• 24:25of population level cohort studies
• 24:27followed by retrospective retrospective
• 24:29selection of patient cases in
• 24:32which to pursue genomic analysis,
• 24:33and this allows us to bypass a common
• 24:35weakness of traditional trials where
• 24:37patients are assigned to specific.
• 24:39Groups and then we wait to see what
• 24:41outcomes they have and this approach
• 24:43we can use the Medicare claims data
• 24:45to cherry pick specific outcomes of
• 24:47interest and then go and pull the tumor
• 24:49samples for the patients who experience
• 24:51these outcomes in the real world
• 24:53and study which treatment patterns,
• 24:55SES factors,
• 24:56or clinical pathologic characteristics
• 24:58appear to be driving those outcomes.
• 25:00And in the case of RRCC proposal,
• 25:02that we're getting ready to
• 25:04submit in February, February,
• 25:05we're going to look at two rare
• 25:07events experienced by patients
• 25:08related to amino therapy.
• 25:10Namely,
• 25:10severe IO toxicities and durable responders,
• 25:13so we're calling this project
• 25:15the virtual siert issue,
• 25:16registry Genomics and Medicare cohort,
• 25:18or a Verge cohort.
• 25:19And as I mentioned,
• 25:20our first application to go in
• 25:22will be in renal cell carcinoma
• 25:23since this study will be following
• 25:25on the heels of my current R 01,
• 25:27but our intention always has been and
• 25:28remains to have several different bridge
• 25:30cohorts across different disease sites.
• 25:32Answering all types of
• 25:34different clinical questions.
• 25:35So in summary,
• 25:36there are many questions relevant
• 25:38to cancer care that can be
• 25:40informed and enhanced by real
• 25:41World Health services research.
• 25:43Many questions cannot be feasibly or
• 25:46ethically addressed by clinical trials alone,
• 25:49and novel linkages may pave
• 25:50the way to novel opportunities
• 25:52in health services research.
• 25:53There are several datasets that
• 25:55are available for research in
• 25:57real world outcomes data and
• 25:59each data has its own strengths,
• 26:01weaknesses,
• 26:02and nuances that you need to know how
• 26:04to work with in order to get the best.
• 26:06And most accurate data and then
• 26:08the incorporation of genomics
• 26:09and biology into health service
• 26:11research is on the horizon.
• 26:13With that,
• 26:13I want to thank the team members
• 26:15who participated in all the various
• 26:16studies that I that I presented today.
• 26:18All of the work I do is team based
• 26:20science and I couldn't do it without
• 26:22the clinical collaborators and the
• 26:24support staff who are helping me with
• 26:25this work. Thank you for your time.
• 26:29Thank you Michaela.
• 26:30Very interesting work.
• 26:32If there are any questions, I I guess
• 26:34what we do is we type them into the chat.
• 26:38While we're waiting now to question.
• 26:40I I thought the most interesting thing
• 26:43he showed was the effect of ZIP code.
• 26:47The five fold increase in mortality.
• 26:49Yes, 'cause of course in within
• 26:51the ZIP code there are many people.
• 26:52There's a range of educational levels,
• 26:55so if you if you just actually broke it down.
• 26:59Are you able to break it down by actual,
• 27:01whether or not a patient
• 27:02has graduated or not?
• 27:03'cause I would assume then that
• 27:05difference would be much greater.
• 27:06Yeah, I mean,
• 27:07so obviously that would be ideal.
• 27:09That's just that's just a limitation
• 27:10of this year Medicare data,
• 27:11so the the SES data is in this
• 27:15available in their Medicare data,
• 27:17and I could talk a whole another
• 27:18half hour about this.
• 27:19Is zipcode level information,
• 27:21so it's not ideal,
• 27:22but it does give you a sense of you.
• 27:24You get zipcode level information
• 27:26about high school graduation,
• 27:27zipcode level, information about poverty.
• 27:29Uhm, about, uh,
• 27:32like the racial or ethnic makeup of
• 27:35a neighborhood somebody lives in.
• 27:37So obviously it's a proxy.
• 27:38It's not ideal,
• 27:39but it's it's better than what's
• 27:40in a lot of other datasets,
• 27:42so it's still
• 27:43despite those very very striking difference.
• 27:46We have a question from Laos.
• 27:48Yes, Titan, congratulations
• 27:50is clearly very exciting.
• 27:51What you described I,
• 27:52I wonder who is your year
• 27:54collaborator Co investigator for the
• 27:56genomic analysts piece of Euro one who
• 28:00will actually do the the sequencing
• 28:02and data analysts and
• 28:03linking to the clinical data.
• 28:04Yeah, so we're still working
• 28:05through the details of that,
• 28:06but we've been talking to all the
• 28:08various cores and thinking about
• 28:10exactly what what we want to do
• 28:12in terms of the genomic analysis.
• 28:15Obviously there's a couple things
• 28:17that are going to weigh in.
• 28:18This is a big study.
• 28:19It like I said, it's going to involve.
• 28:21It's all ecipes from all of the six
• 28:23registries I mentioned are all on board.
• 28:26We're going to have,
• 28:27so that'll be six sites,
• 28:29and so a lot of this unfortunately
• 28:31is gonna be driven by what we
• 28:33can afford in terms of, you know.
• 28:34So we're going to start with a
• 28:36very focused analysis and then
• 28:37from there you know.
• 28:38I'm hoping to build on that with
• 28:40either administrative supplements
• 28:41or other funding mechanisms to
• 28:43build out and expand on that,
• 28:44so that's still that that specific pieces
• 28:46build still being in development right now,
• 28:48but we're.
• 28:49Talking with all the Yale course.
• 28:51There's a lot to follow up
• 28:53with you because you know,
• 28:54I I couldn't write the Yale Genetics
• 28:55Genomics program and you may know
• 28:57that we have a similar large
• 28:58initiative that's run by like Murray.
• 29:01The generations project,
• 29:03and I think there is a lot of synergy
• 29:04that you could you could leverage.
• 29:06Yeah, it'd be great to talk,
• 29:07and we're still we're still
• 29:08developing that specific piece.
• 29:09I would love to talk about it more.
• 29:12Thanks Flash any other questions or comments?
• 29:19How the work is obviously critically
• 29:22dependent on how good the datasets are.
• 29:26Which you have not a lot of control over
• 29:28other than select which ones to use.
• 29:29I mean for example other VA.
• 29:32How does that compare to see?
• 29:33Or how does that compare to Medicare?
• 29:34Or are there systematic differences?
• 29:37Yeah, so great question.
• 29:39Again, I have a whole other talk just
• 29:42talking specifically about these.
• 29:44Uhm, so you know it.
• 29:46It's all about like I said,
• 29:47like knowing the datasets well knowing
• 29:48what their strengths or weaknesses are
• 29:50and knowing how to leverage them so
• 29:52specifically for the wrist ratification
• 29:53grant that I'm talking about where
• 29:54we're going to be using serum,
• 29:55Medicare cipher and the VA data,
• 29:57we're specifically focusing on the
• 29:59variables of interest on things
• 30:00that we know we can get out of.
• 30:02Each of those three datasets, right?
• 30:03So because we want to be able to
• 30:05like develop and then validate
• 30:07the risk prediction algorithms.
• 30:08I mean, I, I said it from the beginning.
• 30:10There's no perfect data set.
• 30:11There are things that are
• 30:12really strong about this year.
• 30:13Medicare data.
• 30:14It is probably the most widely used
• 30:17real-world data set for oncology.
• 30:18Specific research is an
• 30:20incredibly strong data set,
• 30:21but the two big limitations that
• 30:23everyone can tell you right off the
• 30:24top of their head is that it's limited
• 30:26to those who are 65 years and older.
• 30:29It's Medicare only,
• 30:29and then the other limitation is
• 30:31there's a pretty significant lag
• 30:32with the data because it relies on a
• 30:35linkage that's done every two years at NCI,
• 30:36so it's usually about three
• 30:38to four years behind, right?
• 30:39So if you're trying to look
• 30:41at emerging technologies,
• 30:41it can be a little bit of a nuisance.
• 30:43So from the current R 01.
• 30:45Using Seer Medicare data.
• 30:48Actually getting ready to purchase
• 30:49a cohort of the Medicare 100% data.
• 30:51So the limitation to that data set
• 30:53is going to be that it doesn't have
• 30:55the seer registry information,
• 30:56so we're not going to know things
• 30:59like stage or like other clinical
• 31:01pathologic variables.
• 31:02However,
• 31:02the whole you know we're trying
• 31:04to fill in the gaps that we know
• 31:06exist from the previous work that
• 31:07we did with the other datasets,
• 31:09which is the lag that we saw in in this era.
• 31:11Medicare data and the North
• 31:13Carolina cipher data,
• 31:13so we can't look at O as in the
• 31:15context of current immunotherapy,
• 31:17which we know is playing a huge role.
• 31:19In a renal cell carcinoma
• 31:21treatment right now,
• 31:22so the Medicare claims data,
• 31:24while it will have different gaps,
• 31:26is going to allow us to look at other
• 31:29questions alongside of what we've
• 31:30already done to look at how aydelette
• 31:33OAA utilization and adherence looks
• 31:36in the context of amino therapies.
• 31:39So it's just about figuring out,
• 31:40like it's just about acknowledging
• 31:42where the limitations exist,
• 31:43and then figuring out a way to
• 31:45kind of fill that information in.
• 31:48Terrific,
• 31:48thank you very much.
• 31:49Very interesting talk.
• 31:50We need to move on to our second
• 31:53speaker who's Gloria Wong and Gloria
• 31:56is a social professor of OBGYN
• 31:59and reproductive sciences here,
• 32:00and she specialized in the
• 32:02treatment and prevention of ovarian,
• 32:04uterine, and cervical cancers.
• 32:05She's a board certified gynecological
• 32:08oncologist who performs minimally
• 32:10invasive surgery and her research
• 32:11interests are in Dimitriou,
• 32:13SIS associated and ovarian cancer
• 32:14in the prevention and treatment
• 32:16of endometrial cancer recurrence.
• 32:18So Gloria, the floor is yours.
• 32:22Hey, thank you so much for the
• 32:24introduction and I really enjoyed
• 32:25the first talk and learns a lot.
• 32:28So let me just see if I can
• 32:30bring up my slides here.
• 32:36Can you see those? Yes,
• 32:38could you put in presentation? Yes perfect
• 32:42great alright. Well today I wanted
• 32:44to talk about a couple of topics
• 32:47on near and dear to my heart,
• 32:49which is translational science and
• 32:52pivotal trials and gynecological cancer.
• 32:57I have my disclosures on file with the
• 33:00CME office, none of which are related
• 33:02to the content of this presentation.
• 33:07In this talk, I want to first give a
• 33:10epidemia brief overview of the epidemiology
• 33:13and current trends in GYN cancer.
• 33:16Challenges and successes in the
• 33:18field of GYN Cancer Research,
• 33:21including highlighting some
• 33:23recent practice changing trials
• 33:25and example of how translational
• 33:29science in my personal experience,
• 33:31can be a driver for clinical trial
• 33:34development and team science,
• 33:36and then also just touch briefly
• 33:39on some resources available
• 33:41for translational research.
• 33:45And these are the learning objectives.
• 33:52Endometrial cancer has been
• 33:54increasing in both incidence and
• 33:56mortality in the United States.
• 33:58Currently, the lifetime risk of developing
• 34:01under mutual cancer is about one in
• 34:0332 and over 800,000 women in the US
• 34:06are living with endometrial cancer.
• 34:08Ovarian cancer mortality has slightly
• 34:11declined in recent years and currently
• 34:14the lifetime risk of developing
• 34:17ovarian cancer is about one in 83
• 34:20and over 200,000 women in EU S R.
• 34:22Living with ovarian cancer. In EU.
• 34:25S. Thanks to HPV vaccination
• 34:27and cervical screening.
• 34:29The cervical cancer rate has declined
• 34:32over the past decades to about 167 women.
• 34:36However, there are significant
• 34:39disparities related to access
• 34:42of care and affecting outcomes.
• 34:46She whined.
• 34:47Cancers arise from the reproductive
• 34:49tract organs, including the ovary,
• 34:52fallopian tube, uterus,
• 34:53cervix, ***** and vagina,
• 34:55and these organs are remarkable in
• 34:58their ability to respond rapidly
• 35:00to endocrine signals, produce sex,
• 35:03hormones and their remarkable capacity
• 35:05for proliferation, regeneration,
• 35:07and morphological changes,
• 35:08and some of these do relate to
• 35:11underlying risk factors and protective
• 35:13factors for GY and cancers.
• 35:15Full fearing cancer,
• 35:17there's a correlation with increased
• 35:20lifetime ambulatory cycles,
• 35:21whereas oral contraceptive use,
• 35:24pregnancy and risk,
• 35:25and breastfeeding decrease risk.
• 35:29A MWe now that.
• 35:32Term line genetic testing has
• 35:34become much more widespread and may,
• 35:36you know, be available to the general public.
• 35:39It is available now for out of
• 35:42pocket cost for you know about $250
• 35:46to determine if one carries a BRCA
• 35:49one or two mutation and for those
• 35:53patients risk reducing surgery is
• 35:55highly protective for women at average risk.
• 35:58There is a benefit to
• 36:01opportunistic salpingectomy so,
• 36:02uhm,
• 36:03a surgical removal of the flippin
• 36:06tubes at the time of other pelvic
• 36:10surgery for benign indications.
• 36:13Endometrial cancer is linked to the
• 36:18rising obesity rate unopposed estrogen
• 36:21as well as hereditary factors,
• 36:24and we know that use of progestin
• 36:27containing oral contraceptive
• 36:28pills or progestin IUD can offer
• 36:31protection as well as risk reducing
• 36:33surgery for patients at higher risk.
• 36:36And cervical cancer can be really
• 36:41eliminated with widespread implementation
• 36:43of HPV vaccination and cervical screening,
• 36:46which currently consists mainly of
• 36:49liquid cytology and high risk HPV detection.
• 36:54We are still facing notable challenges
• 36:56in the fields of GI and cancer,
• 36:59and I'm going to focus today on and a
• 37:02mutual cancer which has an increasing
• 37:04incidence and mortality rate as well
• 37:07as substantial racial disparity in outcomes.
• 37:11However,
• 37:11this is buffeted by recent successes
• 37:14and pivotal trials in GI and cancer
• 37:17in just in the past 18 months alone,
• 37:20we've seen new first line maintenance
• 37:23therapy options for ovarian cancer.
• 37:25New indications for immunotherapy,
• 37:27including for mismatch repair,
• 37:30proficient at a mutual cancer,
• 37:32as well as new first line and second
• 37:34line standard of care for cervical cancer.
• 37:36So really quite amazing how many.
• 37:40Pivotal trials have resulted in
• 37:43the recent 18 to 24 months leading
• 37:46to practice changing.
• 37:50Approaches,
• 37:51so in 2000 end of 2019 the results
• 37:56of Primon Paolo one were published
• 37:58in the New England Journal,
• 38:01leading to the approval of two different
• 38:04options for first line maintenance
• 38:06therapy of epithelial ovarian cancer.
• 38:08Fallopian tube for primary piratini,
• 38:10oh cancer. Following complete or
• 38:12partial response to first line platinum
• 38:15based chemotherapy, the new rap rib.
• 38:19Demonstrated a significant improvement
• 38:22in progression free survival in both
• 38:25the overall intent to treat population
• 38:28and the homologous recombination
• 38:30deficient population with a hazard risk
• 38:34of 0.43 in progression free survival.
• 38:39Come with clear divergance of the
• 38:43progression free survival curves.
• 38:46Similarly, Palo one which tested elapp rib
• 38:50and bevacizumab for first line maintenance,
• 38:54showed remarkable
• 38:55improvement and progression.
• 38:57Free survival on the upper
• 38:59left in the bracket.
• 39:01Mutated population hazard ratio
• 39:04of 0.31 and on the lower right.
• 39:07Patients without a BRAC mutation.
• 39:09But with a molecular test demonstrating.
• 39:13Homologous recombination deficiency
• 39:16as tested by genomic instability also
• 39:20showed a progression free survival
• 39:23benefit with a hazard ratio of 0.4.
• 39:35And outcomes for patients who,
• 39:37unfortunately often prevent present
• 39:40with advanced stage ovarian cancer,
• 39:42and we know that upon recurrence
• 39:46becomes more difficult to treat and
• 39:49more likely to be chemo resistant.
• 39:54In mutual cancer, just to review some
• 39:57of our recent exciting new options.
• 40:00And this has been really a big deal
• 40:04because actually progress has been
• 40:07quite slow and endometrial cancer.
• 40:10Progestin therapy Megace was approved.
• 40:14You know, many decades ago for
• 40:16palliative treatment of enemy,
• 40:18enemy, troll, and breast cancer.
• 40:21However, really many decades
• 40:23elapsed without any new trials,
• 40:26new indicate indicated therapies for
• 40:29endometrial cancer of a big benefit
• 40:32for our patients without mutual cancer,
• 40:35with seen with the accelerated
• 40:38approval of pembrolizumab.
• 40:40For a minute, solid tumors that were
• 40:44mismatch repair deficient as about
• 40:4720% of endometrial cancers are,
• 40:50or microsatellite instability
• 40:52high or more recently,
• 40:54with the addition of the accelerated
• 40:57approval for the tumor mutation burden high.
• 41:00Uhm?
• 41:00Tumors more recently this.
• 41:04Here we have an additional
• 41:07option for mismatch repair
• 41:09deficient and demetral cancer,
• 41:12just Starla Mob,
• 41:13which received accelerated approval
• 41:16in August and then most recently
• 41:20the keynote 775 updated results were
• 41:24presented at ESMO following previous
• 41:27presentation at SGO showing combination.
• 41:31Of pembrolizumab and lymphatic nib.
• 41:35Showing actually with this combination.
• 41:38In proficient mismatch repair.
• 41:41Proficient endometrial cancers.
• 41:43Uhm,
• 41:44an improvement in overall survival,
• 41:46leading to regular approval of
• 41:49this combination for patients with
• 41:51endometrial cancer that is not
• 41:53MSI high that is mismatch repair
• 41:57proficient and have disease progression
• 41:59following prior systemic therapy.
• 42:05Next, I want to move into how we,
• 42:09as clinicians scientists, participate.
• 42:11And a example for trial in
• 42:15progress that I'd like to share.
• 42:18So I have a couple of different
• 42:20projects moving into clinical trials.
• 42:23This one that's currently in
• 42:26enrolling in clinical trial.
• 42:29And emerged from what began as a
• 42:32collaborative team science project,
• 42:34funded by a narrow one and then
• 42:37another trial, which I'm in the
• 42:41process of moving towards the clinic,
• 42:43which is which I won't talk about today,
• 42:47which was based on translational
• 42:48science done in my lab.
• 42:50Supported by DoD grant.
• 42:52For this study,
• 42:54which began quite a long time ago,
• 42:56UM, I collaborated with, UM,
• 43:01Epidemia Cancer epidemiology experts,
• 43:02and we wanted to ask the
• 43:05question of what could,
• 43:07what we know about the development
• 43:10of endometrial cancer and how
• 43:12obesity is a major risk factor
• 43:15for Type 1 endometrial cancer
• 43:17which has been increasing steadily
• 43:20and underlies the primary.
• 43:22Increase in the endometrial cancer
• 43:25incidence as shown here in this graph.
• 43:30See. A man is dorceau tick tick
• 43:34tick lining rate of hysterectomy
• 43:37is another contributing factor.
• 43:39Uh, what was known?
• 43:41And for many studies,
• 43:42including prospective study of
• 43:44the Women's Health Initiative,
• 43:46that some of the underlying biological
• 43:49mechanisms linking obesity to endometrial
• 43:52cancer include increased estrogen
• 43:55levels increased by availability of
• 43:59estrogens and insulin resistance.
• 44:01Uhm, and the question that we asked was,
• 44:04do these factors that underlie the
• 44:07development of endometrial cancer.
• 44:10Do they play a role in the
• 44:13recurrence and progression of women
• 44:16diagnosed with endometrial cancer?
• 44:18For this study,
• 44:20we utilized the tissue by
• 44:22repository of the GOT 210 study.
• 44:25This is a study that was over
• 44:2960 sites around the USFRGOG
• 44:32Gynaecologic oncology group sites,
• 44:34now under the auspices of NRG
• 44:38Oncology and enrolled patients who
• 44:41were undergoing standard surgical
• 44:43care for endometrial cancer and
• 44:45prospective specimen banking was.
• 44:48Performed and sent to a
• 44:51centralized tissue bank,
• 44:52the jioji tissue bank.
• 44:55And and prospective epidemiological
• 44:58surveys and outcomes.
• 45:00Treatment and outcomes data was
• 45:02obtained in order to facilitate
• 45:04translational research,
• 45:06including a variety of molecular
• 45:09and genetic genomic assays
• 45:12and data integration.
• 45:18So we proposed a study
• 45:21within this G210 cohort,
• 45:24which we obtained funding for,
• 45:27and this focused on the patients
• 45:29who had endometrioid Histology,
• 45:31and we investigated the sex hormone
• 45:34and insulin insulin like growth factor,
• 45:38signaling pathways implicated in the
• 45:41development of endometrial cancer,
• 45:43to determine if these factors.
• 45:45More related to the recurrence or
• 45:48progression of higher risk and a
• 45:51Metroid under mutual cancers and
• 45:53this study included over 800 women,
• 45:55of whom 35% experienced a recurrence
• 45:58in a follow-up of over five years.
• 46:06Or the, UM, the methods?
• 46:10The models were adjusted for known
• 46:12clinical risk factors of recurrence,
• 46:15including age, stage and grade,
• 46:17which were all significant risk factors
• 46:21for recurrence and just to summarize,
• 46:24some of the interesting findings
• 46:27which we presented at an ASCO
• 46:30plenary and we published this
• 46:33year in cancer epidemiol AMPDCEP.
• 46:35We found that circulating estradiol is
• 46:38positively associated with recurrence risk,
• 46:41independent of other factors,
• 46:44and in addition,
• 46:45a particular tissue biomarker that I
• 46:48was interested in based on some of my
• 46:51laboratory research that phosphorylated
• 46:53expression of insulin receptor,
• 46:55IGF one receptor was also independently
• 47:00associated with recurrence risk.
• 47:02And this is an example of immunohistochemical
• 47:06staining for the phosphorylated
• 47:09activated form of the receptor.
• 47:12Because of the, you know,
• 47:14large number of patients we did utilize
• 47:17high throughput approaches for this study,
• 47:19which included construction
• 47:21of tissue microarrays and.
• 47:25And in real time PCR.
• 47:29So the translational impact of these
• 47:31findings is that we identified
• 47:33novel sex hormone and insulin,
• 47:35IGF axis tissue and circulating
• 47:37biomarkers of recurrence in a
• 47:39prospective study of high stage enemy
• 47:42troydan mutual cancer and this led to.
• 47:46A motivation to test strategies to
• 47:49target these pathways for prevention
• 47:52and treatment of endometrial cancer
• 47:54and endometrial cancer recurrence.
• 48:01Come in my lab.
• 48:03We looked at different potential
• 48:07therapies for treating and demetral
• 48:10cancer that could be superior to
• 48:13the previously used strategies.
• 48:15So the most commonly used strategies
• 48:19in in the past have been protesting
• 48:23agents aromat ACE inhibitors or
• 48:26combination tamoxifen and megace,
• 48:29and all of those.
• 48:31Resulted in really modest
• 48:33efficacy with progression.
• 48:35Free survivals even in the first
• 48:37line setting of around three months,
• 48:40so this indicated a need for more effective.
• 48:44Effective approaches for endocrine
• 48:46therapy and we found both in cell
• 48:50line models demonstrate we found that
• 48:53combination cyclin D kinase CDK 46
• 49:00inhibition with AROMATISSE inhibitors
• 49:02was potently synergistic and endometrial
• 49:05cancer cell lines and and this is.
• 49:09Something that it's been very
• 49:12successfully implemented.
• 49:13Of course,
• 49:15in estrogen receptor positive breast cancer.
• 49:18And this just shows in vivo data of
• 49:21showing on the Y axis the tumor volumes
• 49:25of the endometrial cancer xenograft.
• 49:28And this was a RB wild type.
• 49:31As expected,
• 49:32we found that RB mutant mutual
• 49:35cancers are not responsive to this
• 49:37combination and you could see
• 49:40in the red that the combination
• 49:43therapy was significantly superior
• 49:45to either agent alone and.
• 49:48Both and much was really able to
• 49:51inhibit growth of this aggressive
• 49:54endometrial cancer xenografted and this
• 49:57is work we presented at the AACR meeting.
• 50:01And this led me to initiate a collaboration
• 50:04guided by valuable input from,
• 50:07you know my division colleagues here at Yale,
• 50:10who of course are leading clinical
• 50:14researchers as well as colleagues
• 50:17and in breast cancer like Doctor
• 50:22Pusztai and my colleague Dr Santine,
• 50:26incorporating their input,
• 50:27I was able to successfully submit a concept.
• 50:30For a clinical trial for two to be
• 50:36supported by Lilly and in collaboration with.
• 50:41Leading clinical trialists in June
• 50:43ecology and the in the Jioji group,
• 50:46which is our major cooperative
• 50:49group for research.
• 50:51We we actually were able to successfully
• 50:56propose and activate an investigator
• 51:00initiated trial which is GOG 3039,
• 51:03a phase two study of abemaciclib
• 51:05in combination with lectures on
• 51:08advanced recurrent or metastatic
• 51:11endometrioid in Dimitriou cancer.
• 51:13This is a phase two single arm trial
• 51:15to evaluate the efficacy of this
• 51:17drug combination for endometrioid and
• 51:19imaginal cancer with dosing based
• 51:21on the current FDA approval for
• 51:23combination therapy and breast cancer.
• 51:27The study endpoints is to evaluate
• 51:30the efficacy and in addition,
• 51:32the translational research component,
• 51:34which is all being done here at Yale.
• 51:39We are. Collecting longitudinally
• 51:44whole whole blood for cell free DNA as
• 51:49well as FFP of the tissue samples for
• 51:53exploratory analysis and identification
• 51:55of novel biomarkers of response.
• 51:58And how does this trial the JIOJI 3039 trial
• 52:02fit into the rapidly evolving landscape
• 52:05of treatment for endometrial cancer?
• 52:07Well surgery, hysterectomy,
• 52:09removal of the tubes and ovaries,
• 52:12and nodal valuation is still the
• 52:14cornerstone of patients presenting
• 52:16with resectable ended mutual cancer.
• 52:18Following surgery,
• 52:19low end and intermediate risk
• 52:22patients are managed with observation,
• 52:25while high intermediate risk
• 52:27patients standard of care.
• 52:29Some receive radiation therapy or
• 52:31vaginal breakey therapy with the
• 52:33potential benefit of the additional
• 52:35of pembrolizumab for mismatch repair.
• 52:38Deficient patients being evaluated
• 52:40in this trial we have open here,
• 52:44which is the Gio 24 high risk higher
• 52:50risk patients following surgery
• 52:52who are fully respected.
• 52:54Admin therapy includes chemotherapy,
• 52:57usually tax on carboplatin.
• 53:00With a mentor,
• 53:02village individualized radio
• 53:03radiation therapy,
• 53:05often including pelvic radiation,
• 53:06if there's pelvic nodal involvement
• 53:08and whether or not pember Lism AB is
• 53:11going to offer additional benefit
• 53:13to reduce the risk of distant
• 53:16Mets in these higher risk women is
• 53:19being evaluated in keynote.
• 53:21E 21 and what about first line therapy
• 53:24for advanced patients measurable disease,
• 53:28metastatic disease,
• 53:30or recurrent disease?
• 53:32So the standard of care currently is
• 53:36chemotherapy with GOG 209 showing tax sale,
• 53:40CARBO doublet therapy as to double as
• 53:44adopted from ovarian cancer is seems to
• 53:47be more tolerable than triplet therapy.
• 53:50So that's become the standard of care
• 53:52and whether or not pember lism AB.
• 53:55Will improve outcomes in these
• 53:57patients who have a very high
• 53:59risk of progression and recurrence
• 54:02is being evaluated in giot, oh.
• 54:05Eighteen also actively enrolling and
• 54:08in this patient population where NCCN.
• 54:11Guidelines also described hormonal
• 54:13therapy as an option.
• 54:16Would definitely consider Geo G39 for
• 54:19these patients who would be eligible.
• 54:25And what about in the second line setting?
• 54:28Currently we have standard of
• 54:30care options for patients who
• 54:32progressed on previous chemo and
• 54:35those include for mismatch repair,
• 54:37deficient pembrolizumab or just Starla mad.
• 54:41And then for the MMR proficient,
• 54:43we saw that pembrolizumab and inland
• 54:47vatnik combination performed better
• 54:49than physicians choice of second line
• 54:52chemo in the GY and art portfolio.
• 54:55We have a number of biomarker
• 54:58driven therapies being evaluated
• 55:00in a phase two setting,
• 55:02and these are led by Doctor Santine,
• 55:04a fully receptor alpha targeting
• 55:07antibody drug conjugate,
• 55:09as well as a trope 2 targeting anti
• 55:13antibody drug conjugate and certainly
• 55:16for endometrioid endometrial cancer
• 55:19would would would recommend consideration
• 55:23of GOG 39 for these patients.
• 55:26So patients are eligible for GOG 3039
• 55:31with up to two prior systemic regimens,
• 55:34one of which could have been chemo,
• 55:36one of which could have been immunotherapy.
• 55:39And we actually have activated over 20
• 55:43sites of the 25 selected sites and have
• 55:48really been having rapid accrual with the.
• 55:53Current rate of accrual
• 55:55exceeding our expectation of one,
• 55:57and it's currently one to two
• 55:59patients per week.
• 56:00For this trial, which,
• 56:02if it goes to second stage,
• 56:04would enroll a maximum of 52 patients.
• 56:08I just wanted to briefly touch on
• 56:10that since this is relatively new.
• 56:12Is this NCTM navigator or clinical
• 56:15trial specimen resource and it's
• 56:18available for validation of
• 56:20hypotheses following already completed
• 56:23exploratory and pilot studies,
• 56:25and this includes a very vast
• 56:27number of specimens,
• 56:28including a lot of the specimens that were
• 56:31transferred over from the jioji tissue bank,
• 56:34and there is a workflow available.
• 56:38For exploring what specimens are
• 56:42available and submitting for
• 56:45for access to these specimens,
• 56:47for for addressing research questions
• 56:50that may require large number of
• 56:53samples that are collected in a
• 56:56very rigorous way and then,
• 56:59how do we fund translational research
• 57:02in the area of some declining support?
• 57:06One of the mechanisms.
• 57:08Which has been super valuable for
• 57:11supporting translational support.
• 57:14Is this poor mechanism,
• 57:16which of course yellows been very
• 57:18successful and has spores and head and neck,
• 57:21lung, and skin cancer.
• 57:23There are very few GYN funded spores,
• 57:26currently only one and ended meet
• 57:28real one in cervical,
• 57:305 in ovarian and there's one new.
• 57:33Sporen that focuses on health disparities
• 57:37and endometrial Varian cancer.
• 57:40So I hope I've relate some of my
• 57:43enthusiasm for team science and
• 57:45its essential ingredient for
• 57:47translational science and conduct of
• 57:49clinical trials for gene cancers,
• 57:52which are relatively rare
• 57:54cancers and really way for having
• 57:57exciting and meaningful impact.
• 58:00And I hope I've,
• 58:01I hope to yell at people who are
• 58:03interested in collaborating with.
• 58:05Contact me in my emails listed here.
• 58:10Thank you Gloria. Very interesting,
• 58:12very exciting to see the progress
• 58:14that's been made and all these
• 58:16trials that are underway.
• 58:17They're underway, people can please.
• 58:21Type your questions into the chat.
• 58:23While we're waiting, you might want
• 58:24to talk to Roy Herbst if you haven't.
• 58:27He's sort of taking the lead on
• 58:29trying to organize new spores and
• 58:31has quite a bit of experience,
• 58:32so he might be someone to talk to.
• 58:34Be great to have this poor in this
• 58:36in this area in the Piola trial.
• 58:38It it it was comparing bracket positive.
• 58:41Projecting negative patients.
• 58:42Was that bracket one or two or or both?
• 58:45Did they they stratify that?
• 58:49So in the data that was
• 58:52published in the paper,
• 58:54at least not in the main manuscript.
• 58:56I don't recall seeing a stratification
• 59:01of the Braca one versus bracket two.
• 59:05They did show the hazard ratios and
• 59:09PFS curves for a few different groups,
• 59:14and that included the bracket
• 59:17tumor mutation positive.
• 59:19The bracca tumor mutation,
• 59:21positive and HRD positive and
• 59:24then the bracket to mutation.
• 59:27Negative or wild type and HRD
• 59:32positive and then for so.
• 59:35The UM for that trial, the, UM,
• 59:39the benefit was seen in the Braca positive
• 59:44Braca mutated or the HRD positive,
• 59:49which in that trial was
• 59:51determined by the myriad.
• 59:53My choice HRD thing.
• 59:55Uhm and there was not a clinical
• 60:00benefit in the HR proficient.
• 01:00:03Braka wildtype group.
• 01:00:05But that's an interesting question
• 01:00:08about if there are differences
• 01:00:10between Bracha one or two.
• 01:00:13Uhm, mutated,
• 01:00:14which I'm not sure I'll
• 01:00:16look into that though.
• 01:00:17OK, alright, good. There any other
• 01:00:21questions from the audience?
• 01:00:26If not, will thank you Gloria.
• 01:00:28It was very interesting and also Michaela.
• 01:00:29I thought we had a terrific series today
• 01:00:32and we'll see you all next week, bye. I.